Flex is expanding to offer a consumer bill management platform: a hub where users manage both recurring and one-off bills, align payments with their cash flow, and build credit along the way. As we move from a pilot to broader GTM, we're looking for someone who can operate as both the builder of our data foundation and the analyst who turns that data into decisions.

This role will work closely with both business leadership and the core data team to focus on modeling data, digging into consumer behavior, pricing and credit dynamics, and setting the standards the rest of the team builds on.

What you'll do

• Build and maintain the core data models (dbt/Snowflake) that power reporting, experimentation, and ML/credit risk use cases
• Analyze consumer bill payment behavior timing, splitting, delinquency, credit-building outcomes to identify what's working in the pilot and what needs to change
• Partner closely with Product and Engineering to instrument new features (e.g. bill splitting, smart scheduling, credit-building) so we can measure adoption and impact from day one
• Design and analyze experiments that test pricing, credit terms, and product flows, establishing causal links between changes and outcomes
• Create dashboards and self-serve tools that let Product, Risk, and GTM teams track Control Center's pilot-to-scale metrics without needing to come back to Analytics for every question
• Champion data quality, consistent terminology, and documentation as data footprint grows from pilot to full launch
• Set technical direction for how Control Center's data models and pipelines scale as new billers, partners, and product surfaces come online; mentor other analysts/engineers and raise the technical bar across the team

Key qualifications

• 7+ years of experience spanning analytics engineering and data science/analyst work
• Strong SQL and hands-on experience with dbt and Snowflake (or equivalent modern data stack)
• Experience with BI tools (Sigma, Tableau, Mode, Looker, etc.) and building dashboards that non-technical stakeholders use
• Track record designing and analyzing experiments (A/B tests or quasi-experimental methods) to drive product decisions
• Being confident partnering with technical and non technical stakeholders 
• Strong ownership mindset
• Comfort with credit, risk, or fintech data, as well as curiosity about how consumers manage their finances, is a plus